Data-Generating Process Uncertainty: What Difference Does It Make in Portfolio Decisions

• Main Authors: Tu, Jun, Zhou, Guofu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Asset pricing tests: Investments; Data generating process; t distribution; Bayesian analysis; Business
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/2692; https://doi.org/10.1016/j.jfineco.2003.05.003
• Institution: Singapore Management University

As the usual normality assumption is firmly rejected by the data, investors encounter a data-generating process (DGP) uncertainty in making investment decisions. In this paper, we propose a novel way to incorporate uncertainty about the DGP into portfolio analysis. We find that accounting for fat tails leads to nontrivial changes in both parameter estimates and optimal portfolio weights, but the certainty–equivalent losses associated with ignoring fat tails are small. This suggests that the normality assumption works well in evaluating portfolio performance for a mean-variance investor.